Hand-washing logger with health worker category switches

In this, is the latest iteration of the hand-wash logging prototype, I have added four switches. As a registered nurse and ambulance officer I know from first-hand experience how we all hate filling in paperwork. This embedded logger records the date time and duration of a hand-wash event. Although it works very well, I realised I don’t know who is doing the hand-washing. To remedy this I have fitted four switches to the prototype. Each switch represents a category of health worker, namely: nurse, allied health, doctor and other. I will have a stick-on membrane matrix switch strip on the sink for the health worker to push before or during a hand-wash event.

A video of the upgraded logger can be found here: https://youtu.be/mCwyqt1lMmo

The picture below is a mock-up of a hand wash sink at our local hospital with the water proof membrane switch and piezo electric transducer in place.

The logger generates a .csv file that can be imported into Excel.

Nursing semantic networks- a different take on interoperability. Part 4

Part three of this blog introduced the predicate vocabulary ‘FOAF’ which facilitates interoperability on the Semantic Web by using an ‘agreed-upon’ standard, namely the predicate in a triple. In this blog we’ll look at one solution which allows nurses to directly input their knowledge to assist non-clinical ontology designers struggling to decipher seemingly impenetrable clinical semantics. The blog will conclude with a real life input produced by a front-line surgical nurse.

An ontology designer wants the most accurate semantics, preferably direct from the source, in this case, the front-line nurse. In the past, ontology designers relied on ‘third-hand’ semantics from sources other than people who actually use the semantics. Consequently, third-hand semantics may be open to inaccurate interpretation, and it follows, inaccurate ontologies. Ideally, we would like nurses who use the semantics on a day-to-day basis to construct the ontology, unfortunately ‘therein lies the rub’.

The problem with ontologies is that they are written in RDF, a rigid and predictable machine language, obviously, we can’t expect nurses to sit down write out 1000 lines of RDF to describe their nursing environment. To enable nurses to directly input their semantics I introduce an extra step; nurses use a simple ‘node-arc-node’ graph program to construct their clinical environment which can be used to construct an ontology.

A ‘node-arc-node’ graph is a visual representation of the triple. Nurses use a visual knowledge acquisition program to move nodes representing a triple’s subjects and objects about on a screen. Nodes can be connected with an arc which represents a relationship. The program then translates the graphs into RDF which is used to construct the ontology.

My thinking is: the extra step may produce ‘pure’ semantics as opposed to the nurse simply describing what she/he does to an ontology designer. Underpinning the extra step is something I read in the literature; Patricia Benner noted that ‘knowledge is embedded in practice’. Getting nurses thinking about their place in the clinical environment, what they do, who (and what) they interact with, then drawing a graph describing it, may free ‘trapped’ knowledge. Critics may argue that a graph produces a perception, an abstract, or a snapshot of that nurse’s environment. I think our graphs (and their resultant ontologies) may be enhanced if we were to obtain multiple perceptions from nurses working in the same ward, we could triangulate the results to construct a clearer ontology.

An unexpected benefit of drawing graphs and constructing ontologies from them is that the same semantics are produced in two different forms. Semantics in graphs can be analysed by nurses and semantics in ontologies can be analysed by machines. This means that graphs may help nurses introduce or eliminate processes to improve patient care. Also, graphs contain detailed annotations which describe the function and purpose of ‘what nurses do’ to the non-clinician. Machines, on the other hand, can analyse the logical structure and consistency of an ontology and this may open the door to hospital-wide automated auditing.

We asked four nurses who knew nothing about ontologies, graphs or semantics to use the graph software to describe their environments. The nurses worked in the same hospital but in totally different specialities. The nurses produced quite detailed graphs of their respective surgical, emergency, transitional care and administration environments. The graph drawn by the surgical nurse can be viewed here: http://home.spin.net.au/semantic/surgical/surgical_nurse.html

We found that nurses tend to organise their nodes into ‘clusters’.  Clusters in the surgical example include:

  • Medication administration
  • Documentation
  • Referrals to allied health
  • Patient.

We found completely different process structures in each of the specialities, but there was one constant, the nurse’s role in each speciality was predominantly independent. Also, the surgical graph contained surprising ‘hidden’ nursing processes. These include, ‘finding drug keys’ and ‘finding other nurses to check drugs’.

In the next blog instalment I will look at what machine ‘robots’ found when they analysed each of the four ontologies which were constructed from the four graphs.